Data decision gap in organizations leads to inefficient and ineffective decision-making and can lead to many challenges. This gap can be overcome using AI and automation, according to Dan Onions, head of data management solutions, Quantexa.
Fragmented data and disorganized internal systems have plagued companies for years, resulting in ineffective and inefficient decision-making. They’re suffering from a data decision gap, unable to bring together internal and external data to make smart decisions, and the effects can create problems across a company.
More than 95% of organizations suffer from a data decision gap, according to a recent Data in Context research studyOpens a new window by Quantexa, creating issues with regulatory compliance, customer experiences and inefficient use of staff time and talent. The influx of data is endless, and without systems in place to obtain views of real-time data, organizations are at risk for fraud, poor decision-making and financial waste. Fortunately, new technology, used correctly, can help companies overcome the data decision gap, placing vital information in a contextual, automated view to foster smart decision-making from top to bottom.
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Data Decision Gaps Leave Companies in the Dark
The same Data in Context research studyOpens a new window showed organizations face significant issues when working with incomplete or fragmented data sets. Nearly half of respondents said their data decision gap caused issues with regulatory scrutiny and compliance efforts; 44% reported missing customer experience opportunities resulting in retention problems, and 42% reported resource drainage as a result of manual data workload.
Organizations are working with incomplete pictures and a lack of context. That lacking picture makes it hard for organizations to succeed in a world where data will continue to grow exponentially over the next several years. Perhaps the last thing a financial institution wants is to draw the attention and ire of regulators; poor, siloed data invites exactly this. Regulators are increasingly putting pressure on organizations to have better controls on data, and when they see inconsistencies in a company’s data, they’re not afraid to impose fines and implement freezes in operations. These outcomes, then, set a company back not only internally but also with their customers and public reputation.
Siloed data creates difficulties interacting with customers, too. Without a contextual customer view in place, it’s easy for financial and other institutions to overlook customer needs and relationships. This doesn’t have to be the case, however.Â
For example, it’s not uncommon for banks to place account freezes when they spot unusual transfers between accounts. With the right data context in place, however, institutions can easily spot that the transfer is, for example, happening between parent and child to pay for school expenses. Artificial intelligence and automation can detect the customer’s network and similar payments; perhaps the parent hasn’t made direct transfers in large sums to their college-aged child in the past but instead has made small transfers or payments to their university. By spotting these trends, customers can enjoy uninterrupted services and increased satisfaction, leading to overall customer retention and perhaps even an increased use in different types of services.
The data decision gap also creates issues internally, as the talented staff is weighed down by faulty data. Internal investigators are spending valuable time sifting through data sets that, with the right technology in place, could instead be appropriately sorted and put in context to improve, rather than hinder, strategic decision-making.
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Overcoming the Data Decision Gap
With data constantly evolving, it’s crucial for organizations to put effective data management systems in place. Fortunately, automation and artificial intelligence systems already exist, and they’re the key to cleaning up data, catching fraud, avoiding regulatory and compliance issues and utilizing employees’ time and talent in productive ways.
Nearly 40% of companies struggle with inaccuracies in decision-making, bogging down organizations and leaving them in the dark. However, by getting data out of silos and back into context through automated decision-making, company leaders can be sure they’re getting the most out of their data. Data can get old quickly, which means it takes sustained energy and constant monitoring to keep it up to date. It’s a task staff of any size or level of skill simply cannot handle on their own if they want to have real-time information in place.Â
Instead, artificial intelligence and automated programs can put companies in control of their data, giving employees back their time so they can conduct investigations and pursue efforts to create an experience customers will appreciate. The right technology can take fragmented data and make it accessible to individuals across a company, giving multiple people easy-to-understand views of a constant influx of data.
There will always be challenges that need to be solved. But by reducing workload through the use of automation, companies can use their staff’s talents to creatively problem-solve instead of remaining mired in ever-growing piles of data. They can rest assured that their data is up to date and ready to use to make the best decisions possible, overcoming the data decision gap and leading their organizations to success.
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